Electronic apparatus and control method thereof

ABSTRACT

An electronic apparatus and a control method thereof are provided. The control method of the electronic apparatus includes receiving, from a first external electronic apparatus and a second external electronic apparatus, a first artificial intelligence model and a second artificial intelligence model used by the first and second external electronic apparatuses, respectively, and a plurality of learning data stored in the first and second external electronic apparatuses, identifying first learning data, which corresponds to second learning data received from the second external electronic apparatus, among learning data received from the first external electronic apparatus, training the second artificial intelligence model used by the second external electronic apparatus based on the first learning data, and transmitting the trained second artificial intelligence model to the second external electronic apparatus.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. §119(a) of a Korean patent application number 10-2018-0119054, filed onOct. 5, 2018 in the Korean Intellectual Property Office, the disclosureof which is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to an electronic apparatus and a control method.More particularly, the disclosure relates to a method of training anartificial intelligence model of at least one external electronicapparatus among a plurality of external electronic apparatuses based onlearning data stored in the plurality of external electronicapparatuses.

Also, the disclosure relates to an artificial intelligence (AI) systemsimulating functions of a human brain such as cognition anddetermination by utilizing a machine learning algorithm, andapplications thereof.

2. Description of Related Art

An artificial intelligence (AI) system is a computer system implementingintelligence of a human level, and is a system wherein a machine learns,determines, and becomes smarter by itself, unlike rule-based smartsystems. An artificial intelligence system shows a more improvedrecognition rate as it is used more, and becomes capable ofunderstanding user preferences more correctly. For this reason,rule-based smart systems are gradually being replaced by deeplearning-based artificial intelligence systems.

AI technology consists of machine learning (deep learning) and elementtechnologies utilizing machine learning.

Machine learning refers to an algorithm technology ofclassifying/learning the characteristics of input data by itself.Meanwhile, an element technology refers to a technology utilizing amachine learning algorithm such as deep learning, and includes fields oftechnologies such as linguistic understanding, visual understanding,inference/prediction, knowledge representation, and operation control.

Examples of various fields to which artificial intelligence technologiesare applied are as follows. Linguistic understanding refers to atechnology of recognizing languages/characters of humans, andapplying/processing them, and includes natural speech processing,machine translation, communication systems, queries and answers, voicerecognition/synthesis, and the like. Visual understanding refers to atechnology of recognizing an object in a similar manner to human vision,and processing the object, and includes recognition of an object,tracking of an object, search of an image, recognition of humans,understanding of a scene, understanding of a space, improvement of animage, and the like. Inference/prediction refers to a technology ofdetermining information and then making logical inference andprediction, and includes knowledge/probability based inference,optimization prediction, preference based planning, recommendation, andthe like. Knowledge representation refers to a technology ofautomatically processing information of human experiences into knowledgedata, and includes knowledge construction (datageneration/classification), knowledge management (data utilization), andthe like. Operation control refers to a technology of controllingautonomous driving of vehicles and movements of robots, and includesmovement control (navigation, collision, driving), operation control(behavior control), and the like.

Meanwhile, recently, various methods for using an AI model in a devicehaving a small resource like a user device are being discussed. Further,a method for constructing a personalized AI model by training an AImodel with data stored in a user device as learning data is also beingdiscussed.

Recently, a user has various types of user devices. Various userdevices, for example, smartphones, artificial intelligence speakers,digital TVs, refrigerators, etc. may include AI models. However, aproblem has existed, which is that the amount of learning data collectedby some user devices is insufficient, and is insufficient for trainingan AI model, or levels of learning vary for each user device, andaccordingly, even if the same user uses a user device, the same resultvalue of performance cannot be maintained.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure is to addressthe aforementioned problems, and relates to a method of sharing learningdata among a plurality of external electronic apparatuses.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, a control method of anelectronic apparatus for achieving the aforementioned purpose isprovided. The control method includes receiving, from a first externalelectronic apparatus and a second external electronic apparatus, a firstartificial intelligence model and a second artificial intelligence modelused by the first and second external electronic apparatuses,respectively, and a plurality of learning data stored in the first andsecond external electronic apparatuses, identifying first learning data,which corresponds to second learning data received from the secondexternal electronic apparatus, among learning data received from thefirst external electronic apparatus, training the second artificialintelligence model used by the second external electronic apparatusbased on the first learning data, and transmitting the trained secondartificial intelligence model to the second external electronicapparatus.

The control method may further include receiving first and secondcharacteristic information of the first and second external electronicapparatuses, respectively, based on the first characteristic informationof the first external electronic apparatus and the second characteristicinformation of the second external electronic apparatus, converting thefirst learning data into third learning data to train the secondartificial intelligence model used by the second external electronicapparatus, and training the second artificial intelligence model used bythe second external electronic apparatus based on the third learningdata.

Also, in the identifying, at least one of an input value or a labelvalue included in the second learning data may be compared with an inputvalue and a label value included in the learning data received from thefirst external electronic apparatus and the first learning data may beacquired.

The second artificial intelligence model may be an artificialintelligence model for voice recognition, and the plurality of learningdata may include voice data, a label value of the voice data, and userinformation corresponding to the voice data.

Also, in the identifying, at least one of second voice data, a secondlabel value of the second voice data, or second user informationcorresponding to the second voice data included in the second learningdata may be compared with at least one of first voice data, a firstlabel value of the first voice data, and first user informationcorresponding to the first voice data included in the learning datareceived from the first external electronic apparatus and the firstlearning data may be acquired.

The first and second characteristic information may include at least oneof characteristic information related to voice inputters included in thefirst and second external electronic apparatuses, characteristicinformation related to noises input through the voice inputters of thefirst and second external electronic apparatuses, or characteristicinformation related to distances between a location where a user voicewas generated and locations of the first and second external electronicapparatuses.

Meanwhile, in the converting, voice data included in the first learningdata may be converted by using a frequency filter.

Also, in the receiving, based on a predetermined time condition, thefirst and second artificial intelligence models used by the first andsecond external electronic apparatuses, respectively, the plurality oflearning data stored in the first and second external electronicapparatuses, and first and second characteristic information of thefirst and second external electronic apparatuses, respectively, may bereceived.

Meanwhile, an electronic apparatus according to an embodiment of thedisclosure includes a memory, a communicator, and a processor configuredto receive, via the communicator from a first external electronicapparatus and a second external electronic apparatus, a first artificialintelligence model and a second artificial intelligence model used bythe first and second external electronic apparatuses, respectively, anda plurality of learning data stored in the first and second externalelectronic apparatuses, identify first learning data, which correspondsto second learning data received from the second external electronicapparatus, among learning data received from the first externalelectronic apparatus, train the second artificial intelligence modelused by the second external electronic apparatus based on the firstlearning data, and transmit, via the communicator, the trained secondartificial intelligence model to the second external electronicapparatus.

Here, the processor may receive, via the communicator, first and secondcharacteristic information of the first and second external electronicapparatuses, respectively, based on the first characteristic informationof the first external electronic apparatus and the second characteristicinformation of the second external electronic apparatus, convert thefirst learning data into third learning data to train the secondartificial intelligence model used by the second external electronicapparatus, and train the second artificial intelligence model used bythe second external electronic apparatus based on the third learningdata.

Also, the processor may compare at least one of an input value or alabel value included in the second learning data with an input value anda label value included in the learning data received from the firstexternal electronic apparatus and acquire the first learning data.

The second artificial intelligence model may be an artificialintelligence model for voice recognition, and the plurality of learningdata may include voice data, a label value of the voice data, and userinformation corresponding to the voice data.

Also, the processor may compare at least one of second voice data, asecond label value of the second voice data, or second user informationcorresponding to the second voice data included in the second learningdata with at least one of first voice data, a first label value of thefirst voice data, and first user information corresponding to the firstvoice data included in the learning data received from the firstexternal electronic apparatus and acquire the first learning data.

The first and second characteristic information may include at least oneof characteristic information related to voice inputters included in thefirst and second external electronic apparatuses, characteristicinformation related to noises input through the voice inputters of thefirst and second external electronic apparatuses, or characteristicinformation related to distances between a location where a user voicewas generated and locations of the first and second external electronicapparatuses.

Also, the processor may convert voice data included in the firstlearning data by using a frequency filter.

The processor may, based on a predetermined time condition, receive, viathe communicator, the first and second artificial intelligence modelsused by the first and second external electronic apparatuses,respectively, the plurality of learning data stored in the first andsecond external electronic apparatuses, and first and secondcharacteristic information of the first and second external electronicapparatuses, respectively.

Meanwhile, an electronic apparatus according to an embodiment of thedisclosure includes a memory, a communicator, and a processor configuredto receive, via the communicator from external electronic apparatuses, aplurality of learning data stored in the external electronicapparatuses, identify first learning data corresponding to secondlearning data stored in the electronic apparatus among the plurality oflearning data received from the external electronic apparatuses, andtrain an artificial intelligence model used by the electronic apparatusbased on the identified first learning data.

The processor may receive, via the communicator, characteristicinformation of the external electronic apparatuses, based on thecharacteristic information of the external electronic apparatuses andcharacteristic information of the electronic apparatus, convert thefirst learning data into third learning data to train the artificialintelligence model used by the electronic apparatus, and train theartificial intelligence model used by the electronic apparatus based onthe third learning data.

Also, the processor may compare at least one of an input value or alabel value included in the second learning data with an input value anda label value included in the plurality of learning data received fromthe external electronic apparatuses and identify the first learningdata.

The characteristic information of the electronic apparatus and theexternal electronic apparatuses may include at least one ofcharacteristic information related to voice inputters of the electronicapparatus and the external electronic apparatuses, characteristicinformation related to noises input through the voice inputters of theelectronic apparatus and the external electronic apparatuses, orcharacteristic information related to distances between a location wherea user voice was generated and locations of the electronic apparatus andthe external electronic apparatuses.

According to the aforementioned various embodiments of the disclosure,an electronic apparatus can provide learning data suitable for anexternal electronic apparatus wherein personalized learning data isinsufficient, and train an artificial intelligence model by using theprovided learning data.

Other aspects, advantages, and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a diagram schematically illustrating an embodiment of thedisclosure;

FIG. 2 is a block diagram illustrating a schematic configuration of anelectronic apparatus according to an embodiment of the disclosure;

FIG. 3 is a block diagram illustrating a detailed configuration of anelectronic apparatus according to an embodiment of the disclosure;

FIG. 4 is a diagram illustrating a data sharing method according to anembodiment of the disclosure;

FIG. 5 is a diagram illustrating a data sharing method according to anembodiment of the disclosure;

FIG. 6 is a diagram illustrating a method of converting learning dataaccording to an embodiment of the disclosure;

FIG. 7 is a diagram illustrating a method of acquiring learning dataaccording to an embodiment of the disclosure;

FIG. 8 is a diagram illustrating a method of sharing learning dataaccording to an embodiment of the disclosure;

FIG. 9 is a diagram illustrating a method for a second externalelectronic apparatus to convert learning data according to an embodimentof the disclosure; and

FIG. 10 is a flow chart illustrating a control method of an electronicapparatus according to an embodiment of the disclosure.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components, and structures.

DETAILED DESCRIPTION

The following described with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of thedisclosure is provided for illustration purpose only and not for thepurpose of limiting the disclosure as defined by the appended claims andtheir equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

In the disclosure, expressions such as “have,” “may have,” “include,”and “may include” should be construed as denoting that there are suchcharacteristics (e.g., elements such as numerical values, functions,operations and components), and the terms are not intended to excludethe existence of additional characteristics.

Also, in the disclosure, the expressions “A or B,” “at least one of Aand/or B,” or “one or more of A and/or B” and the like may include allpossible combinations of the listed items. For example, “A or B,” “atleast one of A and B,” or “at least one of A or B” refer to all of thefollowing cases: (1) including at least one A, (2) including at leastone B, or (3) including at least one A and at least one B.

Further, the expressions “first,” “second,” and the like used in thedisclosure may be used to describe various elements regardless of anyorder and/or degree of importance. Also, such expressions are used onlyto distinguish one element from another element, and are not intended tolimit the elements.

Also, the description in the disclosure that one element (e.g., a firstelement) is “(operatively or communicatively) coupled with/to” or“connected to” another element (e.g., a second element) should beinterpreted to mean that the one element may be directly coupled to theanother element, or the one element may be coupled to the anotherelement through another element (e.g., a third element). In contrast,the description that one element (e.g., a first element) is “directlycoupled” or “directly connected” to another element (e.g., a secondelement) can be interpreted to mean that another further element (e.g.,a third element) does not exist between the one element and otherelement.

In addition, the expression “configured to” used in the disclosure maybe interchangeably used with other expressions such as “suitable for,”“having the capacity to,” “designed to,” “adapted to,” “made to,” and“capable of,” depending on cases. Meanwhile, the term “configured to”does not necessarily mean that a device is “specifically designed to” interms of hardware. Instead, under some circumstances, the expression “adevice configured to” may mean that the device “is capable of”performing an operation together with another device or component. Forexample, the phrase “a sub-processor configured to perform A, B and C”may mean a dedicated processor (e.g., an embedded processor) forperforming the corresponding operations, or a generic-purpose processor(e.g., a central processing unit (CPU) or an application processor) thatcan perform the corresponding operations by executing one or moresoftware programs stored in a memory device.

An electronic apparatus according to various embodiments of thedisclosure may include, at least one of, for example, a smartphone, atablet personal computer (PC), a mobile phone, a video phone, an e-bookreader, a desktop PC, a laptop PC, a netbook computer, a workstation, aserver, a personal digital assistant (PDA), a portable multimedia player(PMP), a moving picture experts group phase 1 or phase 2 (MPEG-1 orMPEG-2) audio layer 3 (MP3) player, a medical instrument, a camera, or awearable device. Meanwhile, a wearable device may include at least oneof an accessory-type device (e.g., a watch, a ring, a bracelet, an anklebracelet, a necklace, glasses, a contact lens, or a head-mounted-device(HMD)), a device integrated with fabrics or clothing (e.g., electronicclothing), a body-attached device (e.g., a skin pad or a tattoo), or animplantable circuit. Also, in some embodiments, an electronic apparatusaccording to various embodiments of the disclosure may include at leastone of, for example, a television, a digital versatile disk (DVD)player, an audio, a refrigerator, an air conditioner, a cleaner, anoven, a microwave oven, a washing machine, an air cleaner, a set topbox, a home automation control panel, a security control panel, a mediabox (e.g., Samsung HomeSync™, Apple TV™, or Google TV™), a game console(e.g., Xbox™, PlayStation™), an electronic dictionary, an electronickey, a camcorder, or an electronic photo frame.

According to another embodiment of the disclosure, an electronicapparatus may include at least one of various types of medicalinstruments (e.g., various types of portable medical measurementinstruments (a blood glucose meter, a heart rate meter, a blood pressuremeter, or a thermometer, etc.), magnetic resonance angiography (MRA),magnetic resonance imaging (MRI), computed tomography (CT), aphotographing device, an ultrasonic instrument, etc.), a navigationdevice, a global navigation satellite system (GNSS), an event datarecorder (EDR), a flight data recorder (FDR), a vehicle infotainmentdevice, an electronic device for vessels (e.g., a navigation device forvessels, a gyrocompass, etc.), avionics, a security device, a head unitfor a vehicle, an industrial or a household robot, a drone, an automatedteller machine (ATM) of a financial institution, a point of sales (POS)of a store, or an Internet of things (IoT) device (e.g., a light bulb,various types of sensors, a sprinkler device, a fire alarm, athermostat, a street light, a toaster, exercise equipment, a hot watertank, a heater, a boiler, etc.).

Also, in the disclosure, the term “user” may refer to a person who usesan electronic apparatus or an apparatus using an electronic apparatus(e.g., an artificial intelligence electronic apparatus).

Meanwhile, in the disclosure, a first artificial intelligence model maymean an artificial intelligence model used by a first externalelectronic apparatus or an artificial intelligence model received from afirst external electronic apparatus, and a second artificialintelligence model may mean an artificial intelligence model used by asecond external electronic apparatus or an artificial intelligence modelreceived from a second external electronic apparatus.

Meanwhile, in the disclosure, first learning data may mean learning datastored in a first external electronic apparatus, and second learningdata may mean learning data stored in a second external electronicapparatus.

Hereinafter, the disclosure will be described in more detail withreference to the accompanying drawings.

FIG. 1 is a diagram schematically illustrating an embodiment of thedisclosure.

Referring to FIG. 1 , a plurality of external electronic apparatuses(e.g., a first external electronic apparatus 200-1, a second externalelectronic apparatus 200-2, and a third external electronic apparatus200-3) may communicate with an electronic apparatus 100. The pluralityof external electronic apparatuses may include artificial intelligencemodels, and may be apparatuses for providing various services by usingartificial intelligence models. Here, artificial intelligence modelsused by the plurality of external electronic apparatuses may varyaccording to the purpose of each of the plurality of external electronicapparatuses. For example, an artificial intelligence model may vary suchas an artificial intelligence model for voice recognition, an artificialintelligence model for image analysis, etc. The electronic apparatus 100is an apparatus for training artificial intelligence models used by theplurality of external electronic apparatuses.

Specifically, the electronic apparatus 100 may store artificialintelligence models used by the plurality of external electronicapparatuses, and data necessary for training the artificial intelligencemodels. Specifically, the electronic apparatus 100 may receive theartificial intelligence model of each of the plurality of externalelectronic apparatuses from the plurality of external electronicapparatuses, and store the models. Here, the artificial intelligencemodel of each of the plurality of external electronic apparatuses may bean artificial intelligence model trained based on learning data of eachof the plurality of external electronic apparatuses. However, thedisclosure is not limited thereto, and the electronic apparatus 100 canreceive an artificial intelligence model used by each of the pluralityof external electronic apparatuses from an external server, and storethe model.

Meanwhile, the electronic apparatus 100 may receive learning data fromeach of the plurality of external electronic apparatuses, and store thedata. Here, the electronic apparatus 100 may classify artificialintelligence models and learning data received from each of theplurality of external electronic apparatuses by each external electronicapparatus, and store them.

The electronic apparatus 100 may train the artificial intelligence modelused by each of the plurality of external electronic apparatuses basedon the learning data received from each of the plurality of externalelectronic apparatuses. For example, the electronic apparatus 100 maytrain the first artificial intelligence model used by the first externalelectronic apparatus 200-1 based on the learning data received from thefirst to third external electronic apparatuses 200-1 to 200-3. By thesame method, the electronic apparatus 100 may train the secondartificial intelligence model used by the second external electronicapparatus 200-2 based on the learning data received from the first tothird external electronic apparatuses 200-1 to 200-3. Also, by the samemethod, the electronic apparatus 100 may train the third artificialintelligence model used by the third external electronic apparatus 200-3based on the learning data received from the first to third externalelectronic apparatuses 200-1 to 200-3.

Through the aforementioned method, the plurality of external electronicapparatuses may share learning data stored in each of the apparatusesand construct personalized artificial intelligence models. Also, forexample, the first external electronic apparatus 200-1 is an apparatuswhere there is a lot of stored learning data. In addition, in case thelearning data stored in the second external electronic apparatus 200-2and the third external electronic apparatus 200-3 is small compared tothe first external electronic apparatus 200-1, there would be an effectthat the second external electronic apparatus 200-2 and the thirdexternal electronic apparatus 200-3 can train artificial intelligencemodels by using the learning data stored from the first externalelectronic apparatus 200-1 for solving the problem of insufficientlearning data.

Meanwhile, artificial intelligence models sharing learning data asmentioned above may be artificial intelligence models performing similarfunctions. For example, all of the first to third artificialintelligence models may be artificial intelligence models related tovoice recognition. Alternatively, all of the first to third artificialintelligence models may be artificial intelligence models for imageanalysis. However, the disclosure is not limited thereto, and in casethere is a need to share learning data, learning data stored byartificial intelligence models different from one another can be sharedby the plurality of external electronic apparatuses.

Meanwhile, the electronic apparatus 100 can train artificialintelligence models of the plurality of external electronic apparatusesby using an external server storing various learning data. In this case,training artificial intelligence models by using all of the learningdata of the external server has a disadvantage that a large amount ofresources are needed. Accordingly, the electronic apparatus 100 mayselect learning data similar to the learning data included in the firstto third external electronic apparatuses 200-1 to 200-3 among the vastamount of learning data stored by the external server, and trainartificial intelligence models based on the selected learning data.

Hereinafter, various embodiments of the disclosure will be describedbased on the first external electronic apparatus 200-1 and the secondexternal electronic apparatus 200-2 among the plurality of externalelectronic apparatuses. Meanwhile, the technical idea of the disclosurecan be applied to two or more external electronic apparatuses.

FIG. 2 is a block diagram illustrating a schematic configuration of anelectronic apparatus according to an embodiment of the disclosure.

The electronic apparatus 100 may include a memory 110, a communicator120, and a processor 130.

The memory 110 may store instructions or data related to at least oneother different component of the electronic apparatus 100. Inparticular, the memory 110 may be implemented as a nonvolatile memory, avolatile memory, a flash memory, a hard disk drive (HDD), or a solidstate drive (SSD), etc. The memory 110 may be accessed by the processor130, and reading/recording/correction/deletion/update, etc. of data bythe processor 130 may be performed. In the disclosure, the term memorymay include the memory 110, the read-only memory (ROM) (not shown) andrandom access memory (RAM) (not shown) inside the processor 130, or amemory card (not shown) mounted on the electronic apparatus 100 (e.g., amicro secure digital (SD) card, a memory stick).

In addition, the memory 110 may receive artificial intelligence modelsused by the first external electronic apparatus 200-1 and the secondexternal electronic apparatus 200-2 from the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2, andlearning data stored in the first external electronic apparatus 200-1and the second external electronic apparatus 200-2. Further, the memory110 may receive information on characteristic information of the firstexternal electronic apparatus 200-1 and the second external electronicapparatus 200-2, and store the information. Here, characteristicinformation of the first external electronic apparatus 200-1 and thesecond external electronic apparatus 200-2 may include at least one ofcharacteristic information related to the voice inputters included inthe first external electronic apparatus 200-1 and the second externalelectronic apparatus 200-2, characteristic information related to noisesinput through the voice inputters of the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2, orcharacteristic information related to the distance between a locationwherein a user voice was generated and the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2.

The communicator 120 is a component for performing communication withanother electronic apparatus. Meanwhile, communicative connection of thecommunicator 120 with another electronic apparatus may includecommunication through a third apparatus (e.g., a repeater, a hub, anaccess point, a server, a gateway, etc.). Wireless communication mayinclude cellular communication using at least one of long term evolution(LTE), LTE Advance (LTE-A), code division multiple access (CDMA),wideband CDMA (WCDMA), a universal mobile telecommunications system(UMTS), Wireless Broadband (WiBro), or a Global System for MobileCommunications (GSM). According to an embodiment, wireless communicationmay include, for example, at least one of Wi-Fi, Bluetooth, Bluetoothlow energy (BLE), Zigbee, near field communication (NFC), MagneticSecure Transmission, radio frequency (RF), or a body area network (BAN).Wired communication may include, for example, at least one of auniversal serial bus (USB), a high definition multimedia interface(HDMI), a recommended standard 232 (RS-232), power line communication,or a plain old telephone service (POTS). Networks wherein wirelesscommunication or wired communication is performed may include at leastone of a telecommunication network, for example, a computer network(e.g., a local area network (LAN) or a wide area network (WAN)),Internet, or a telephone network.

Specifically, the communicator 120 may receive artificial intelligencemodels used by the first external electronic apparatus 200-1 and thesecond external electronic apparatus 200-2 from the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2, and learning data stored in the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2.

The processor 130 may be electronically connected with the memory 110,and control the overall operations and functions of the electronicapparatus 100.

Specifically, the processor 130 may identify first learning datacorresponding to second learning data received from the second externalelectronic apparatus 200-2 among a plurality of learning data receivedfrom the first external electronic apparatus 200-1, and train theartificial intelligence model used by the second external electronicapparatus 200-2 based on the identified first learning data. In thiscase, the first external electronic apparatus 200-1 may be an externalserver including a vast amount of learning data, and the second externalelectronic apparatus 200-2 may be a user terminal.

Meanwhile, the processor 130 may receive characteristic information ofeach of the first external electronic apparatus 200-1 and the secondexternal electronic apparatus 200-2 from the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2. Theelectronic apparatus 100 may convert the first learning data into thirdlearning data for training the second artificial intelligence modelbased on the characteristic information of the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2.

As an example, in case the second artificial intelligence model is anartificial intelligence model for voice recognition, learning data maybe data for a user voice. Here, user voices received by the firstexternal electronic apparatus 200-1 and the second external electronicapparatus 200-2 may vary according to characteristic information of eachexternal electronic apparatus. For example, a case where the firstexternal electronic apparatus 200-1 is a user terminal such as asmartphone, and the second external electronic apparatus 200-2 is arefrigerator may be assumed. The first external electronic apparatus200-1 and the second external electronic apparatus 200-2 may bedifferent from each other in the types of microphones included insidethe apparatuses, the number of microphones, etc., and also, thedistances from the starting point of a user voice to the externalelectronic apparatuses may be different. Accordingly, the noise,frequency characteristics, etc. included in a user voice received by thefirst external electronic apparatus 200-1 may be different from thenoise, frequency characteristics, etc. included in a user voice receivedby the second external electronic apparatus 200-2. Thus, in case theelectronic apparatus 100 is going to use the second artificialintelligence model, there is a need to convert the first learning datato suit the characteristics of the second external electronic apparatus200-2. Accordingly, the processor 130 may convert the first learningdata into third learning data for training the second artificialintelligence model based on the characteristic information of the firstexternal electronic apparatus 200-1 and the second external electronicapparatus 200-2.

In the aforementioned embodiment, a case where an artificialintelligence model is for voice recognition was described. However, inthe case of an artificial intelligence model performing a differentfunction, the same technical idea can be applied depending on needs.

Meanwhile, the processor 130 may compare at least one of an input valueor a label value included in the second learning data with an inputvalue and a label value included in each of the plurality of learningdata received from the first external electronic apparatus 200-1 andacquire first learning data. Specifically, in case the first externalelectronic apparatus 200-1 is an external server including a vast amountof learning data, the electronic apparatus 100 may identify learningdata for training the second artificial intelligence model, among thelearning data of the first external electronic apparatus 200-1. In thiscase, the electronic apparatus 100 may identify learning data similar tothe input value and the label value of the second learning data from theplurality of learning data of the first external electronic apparatus200-1.

As an example, in case the first and second artificial intelligencemodels are artificial intelligence models for voice recognition, thefirst and second learning data may include user voice data, a labelvalue of the voice data, and user information corresponding to the voicedata. Here, an input value may be data related to an acoustic model, anda label value may be data related to a language model, and userinformation may be user identification information. Specifically, aninput value may be data such as the waveform of a user voice, and mayinclude data such as intonation and tone, and a label value may be textdata wherein a user voice was converted into a text. Also, foridentifying learning data similar to the input value and the label valueof the second learning data from the plurality of learning data of thefirst external electronic apparatus 200-1, the electronic apparatus 100may identify whether waveforms of input values are similar, ordistribution of label values expressed in a vector format exists withina predetermined distance.

Meanwhile, the processor 130 may convert voice data included in thefirst learning data by using a frequency filter. Specifically, theprocessor 130 may acquire a frequency filter that can generate voicedata appropriate for the second external electronic apparatus 200-2based on characteristic information of the first external electronicapparatus 200-1 and characteristic information of the second externalelectronic apparatus 200-2, and acquire third learning data by using theacquired filter.

Also, the processor 130 may perform the aforementioned operations incase a predetermined time condition is satisfied. For example, theprocessor 130 may receive learning data from the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2 around dawn and train the first artificial intelligence model andthe second artificial intelligence model.

Meanwhile, according to another embodiment of the disclosure, the firstartificial intelligence model and the second artificial intelligencemodel may be artificial intelligence models for understanding of naturallanguages. In this case, learning data may be information on the purportof a user voice and a slot included in a result of voice recognitionacquired through a voice recognition model.

FIG. 3 is a block diagram illustrating a detailed configuration of anelectronic apparatus according to an embodiment of the disclosure.

Referring to FIG. 3 , the electronic apparatus 100 may further includean inputter 140, a display 150, and an audio outputter 160 in additionto the memory 110, the communicator 120, and the processor 130. However,the components are not limited to the aforementioned components, andsome components may be added or omitted depending on needs.

The inputter 140 is a component for receiving input of a userinstruction. Here, the inputter 140 may include a camera 141, amicrophone 142, a touch panel 143, etc. The camera 141 is a componentfor acquiring image data around the electronic apparatus 100. The camera141 may photograph a still image and a moving image. For example, thecamera 141 may include one or more image sensors (e.g., a front surfacesensor or a back surface sensor), a lens, an image signal processor(ISP), or a flash (e.g., a light emitting diode (LED), a xenon lamp,etc.). The microphone 142 is a component for acquiring sounds around theelectronic apparatus 100. The microphone 142 may receive input of anacoustic signal outside, and generate electronic voice information.Also, the microphone 142 may use various noise removal algorithms forremoving noises generated in the process of receiving input of anacoustic signal outside. The touch panel 143 is a component forreceiving various user inputs. The touch panel 143 may receive input ofdata by a user manipulation. Also, the touch panel 143 may beconstituted while being combined with the display that will be describedbelow. Meanwhile, it is obvious that the inputter 140 may includevarious components for receiving input of various data in addition tothe camera 141, the microphone 142, and the touch panel 143.

The aforementioned various components of the inputter 140 may be used invarious forms. For example, in case the first external electronicapparatus 200-1 includes a voice recognition model, but the secondexternal electronic apparatus 200-2 does not include a voice recognitionmodel, and the second external electronic apparatus 200-2 needs a resultfor voice recognition, the electronic apparatus 100 may input a uservoice input through the microphone 142 into the first artificialintelligence model received from the first external electronic apparatus200-1 and output a result value, and transmit the value to the secondexternal electronic apparatus 200-2. That is, the electronic apparatus100 may not only share learning data between the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2, but also acquire a result value for an input value of anartificial intelligence model not included in each external electronicapparatus instead of the external electronic apparatuses, and transmitthe result value.

The display 150 is a component for outputting various images. Thedisplay 150 for providing various images may be implemented as displaypanels in various forms. For example, the display panel may beimplemented as various display technologies such as a liquid crystaldisplay (LCD), organic light emitting diodes (OLEDs), an active-matrixorganic light-emitting diode (AM-OLED), liquid crystal on silicon(LcoS), digital light processing (DLP), etc. Also, the display 150 maybe combined with at least one of the front surface area, the sidesurface area, or the back surface area of the electronic apparatus 100,in the form of a flexible display.

Specifically, the display 150 may display various setting informationfor sharing learning data among external electronic apparatuses. Thatis, sharing of learning data among external electronic apparatuses maybe performed automatically, but may also be performed by a userinstruction, and the display 150 may output various information forreceiving input of a user instruction or inquiring about a userinstruction.

The audio outputter 160 is a component outputting various kinds ofnotification sounds or voice messages as well as various types of audiodata for which various processing operations such as decoding oramplification, noise filtering, etc. were performed by an audioprocessor. The audio processor is a component performing processing ofaudio data. At the audio processor, various processing such as decodingor amplification, noise filtering, etc. of audio data may be performed.Audio data processed at the audio processor may be output to the audiooutputter 160. In particular, the audio outputter may be implemented asa speaker, but this is merely an example, and the audio outputter may beimplemented as an output terminal that can output audio data.

The processor 130 controls the overall operations of the electronicapparatus 100, as described above. The processor 130 may include a RAM131, a ROM 132, a main CPU 134, a graphic processor 133, first to nthinterfaces 135-1 to 135-n, and a bus 136. Here, the RAM 131, the ROM132, the main CPU 134, the graphic processor 133, and the first to nthinterfaces 135-1 to 135-n may be connected with one another through thebus 136.

The ROM 132 stores a set of instructions, etc. for system booting. Whena turn-on instruction is input and power is supplied, the main CPU 134copies an operating system (O/S) stored in the memory in the RAM 131according to the instruction stored in the ROM 132, and boots the systemby executing the O/S. When booting is completed, the main CPU 134 copiesvarious types of application programs stored in the memory in the RAM131, and performs various types of operations by executing theapplication programs copied in the RAM 131.

Specifically, the main CPU 134 accesses the memory 110, and performsbooting by using the O/S stored in the memory 110. Then, the main CPU134 performs various operations by using various programs, contents,data, etc. stored in the memory 110.

The first to nth interfaces 135-1 to 135-n are connected with theaforementioned various components. One of the interfaces may be anetwork interface connected to an external electronic apparatus througha network.

Hereinafter, various embodiments according to the disclosure will bedescribed with reference to FIGS. 4 to 8 .

FIGS. 4 and 5 are diagrams illustrating a data sharing method accordingto various embodiments of the disclosure.

Specifically, FIG. 4 is an diagram illustrating a method of selectinglearning data necessary for training the second artificial intelligencemodel in case the first external electronic apparatus 200-1 is anexternal server storing various and vast learning data, and FIG. 5 is adiagram illustrating a method of selecting learning data necessary fortraining the second artificial intelligence model in case the firstexternal electronic apparatus 200-1 is a personal terminal storingpersonalized learning data.

Referring to FIG. 4 , the first external electronic apparatus 200-1 mayinclude the first artificial intelligence model and the first database,and the second external electronic apparatus 200-2 may include thesecond artificial intelligence model and the second database. Here, thefirst database and the second database may store a plurality of learningdata.

First, a selection module may acquire the first learning data similar tothe second learning data (including an input value D2 Input and a labelvalue D2 Label) including an input value and a label value among theplurality of learning data of the second database from the plurality oflearning data of the first database. Specifically, as illustrated inFIG. 4 , the selection module may compare at least one of the inputvalue D2 Input or the label value D2 Label of the second learning datawith at least one of the input value or the label value of each of theplurality of learning data of the first database, and select similardata.

The selection module may train the second artificial intelligence modelas the first learning data including an input value D1 Input and a labelvalue D1 Label similar to the second learning data as an input value.

As in the aforementioned method, the electronic apparatus 100 may trainthe second artificial intelligence model by acquiring learning datasimilar to each of the plurality of learning data of the second databasefrom the first database.

Referring to FIG. 5 , the electronic apparatus 100 may use all of theplurality of learning data of the first database as learning data fortraining the second artificial intelligence model. That is, unlike thecase in FIG. 4 , in case both of the learning data stored in the firstexternal electronic apparatus 200-1 and the second external electronicapparatus 200-2 are learning data received from a user, the electronicapparatus 100 may train the second artificial intelligence model byusing all of the plurality of learning data of the first database.

FIG. 6 is a diagram illustrating a method of converting learning dataaccording to an embodiment of the disclosure.

Referring to FIG. 6 , the selection module may acquire the firstlearning data similar to the second learning data (including an inputvalue D2 Input and a label value D2 Label) including an input value anda label value among the plurality of learning data of the seconddatabase from the plurality of learning data of the first database.Specifically, as illustrated in FIG. 4 , the selection module maycompare at least one of the input value D2 Input or the label value D2Label of the second learning data with at least one of the input valueor the label value of each of the plurality of learning data of thefirst database, and select similar data.

The selection module may acquire the first learning data including theinput value D1 Input and the label value D1 Label similar to the secondlearning data, and a conversion module may acquire third learning data(an input value D1′ Input and a label value D1′ Label) based on theinput value D1 Input, the input value D2 Input, and the label value D1.That is, as the third learning data, learning data appropriate for thesecond external electronic apparatus 200-2 may be acquired by convertingthe first learning data.

As an embodiment for conversion of learning data, in case the firstlearning data and the second learning data are data for trainingartificial intelligence models, the D1 Input and the D2 Input may bedata related to a user voice received by each external electronicapparatus.

Specifically, the electronic apparatus 100 may add data for noisesaround the first external electronic apparatus to the second learningdata (specifically, the D2 Input), and acquire third learning data(specifically, the D r Input). For example, the electronic apparatus 100may acquire data for the ambient noises of the first external electronicapparatus 200-1 according to the usage environment of the first externalelectronic apparatus 200-1, and add the acquired data for the ambientnoises of the first external electronic apparatus 200-1 and the D2 Inputin the time area. As another example, the electronic apparatus 100 mayfilter the D2 Input in the frequency area by using the frequency filterfor the ambient noise environment of the first external electronicapparatus 200-1. As another example, the electronic apparatus 100 mayacquire data for a non-voice section included in the D1 Input by usingvoice activity detection (VAD), and add data for the non-voice sectionand the D2 input in the time area. Here, VAD is a technology of dividinga portion where a user voice is included and a portion where a uservoice is not included (a mute portion or a non-voice portion) in aninput user utterance, and the electronic apparatus 100 may acquire thirdlearning data by using the noise environment information included in thefirst external electronic apparatus 200-1 by using the non-voiceportion.

As another example, the electronic apparatus 100 may acquire thirdlearning data based on characteristic information of the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2.

Specifically, in case characteristic information of the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2 is specification information for the first external electronicapparatus 200-1 and the second external electronic apparatus 200-2, theelectronic apparatus 100 may acquire third learning data inconsideration of the difference between the specification information ofthe first external electronic apparatus 200-1 and the specificationinformation of the second external electronic apparatus 200-2.

Alternatively, in case characteristic information of the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2 is characteristic information related to noises input through thefirst external electronic apparatus 200-1 and the second externalelectronic apparatus 200-2, the electronic apparatus 100 may acquirethird learning data by using the difference between the noise input intothe first external electronic apparatus 200-1 and the noise input intothe second external electronic apparatus 200-2.

Alternatively, in case characteristic information of the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2 is distance information between the location of the first externalelectronic apparatus 200-1 and a location wherein a user voice wasgenerated and the distance information between the location of thesecond external electronic apparatus 200-2 and a location wherein a uservoice was generated, the electronic apparatus 100 may acquire thirdlearning data based on the distance information between the location ofthe first external electronic apparatus 200-1 and a location wherein auser voice was generated and the distance information between thelocation of the second external electronic apparatus 200-2 and alocation wherein a user voice was generated. For example, a case whereina user voice is input into the first external electronic apparatus 200-1in a short distance, and a user voice is input into the second externalelectronic apparatus 200-2 from a far distance can be assumed. Ingeneral, a characteristic exists which is that, in case a user voice isinput from a far distance, in an audio signal received by an apparatus,a low frequency portion is emphasized, and in case a user voice is inputin a short distance, in an audio signal received by an apparatus, a highfrequency portion is emphasized. Accordingly, for converting an audiosignal corresponding to a user voice received by the first externalelectronic apparatus 200-1 into an audio signal appropriate for thesecond external electronic apparatus 200-2, signal processing such asreducing the high frequency area of the audio signal corresponding tothe user voice received by the first external electronic apparatus200-1, and increasing the low frequency portion, etc. is needed. Thus,the electronic apparatus 100 may convert the third learning data inconsideration of the distance information between the location of thefirst external electronic apparatus 200-1 and a location wherein a uservoice was generated and the distance information between the location ofthe second external electronic apparatus 200-2 and a location wherein auser voice was generated, in consideration of the aforementioned matter.As described above, for converting the first learning data into thethird learning data, the electronic apparatus 100 may use a frequencyfilter.

Meanwhile, the electronic apparatus 100 may convert all of the pluralityof learning data of the first database to suit the characteristicinformation of the second external electronic apparatus 200-2, asdescribed in FIG. 5 . That is, in FIG. 6 , an embodiment where datasimilar to the second learning data is acquired, and the acquiredlearning data is changed was described, but the disclosure is notlimited thereto, and the electronic apparatus 100 can convert alllearning data of the first database, without a process of acquiringlearning data similar to the second learning data.

FIG. 7 is a diagram for illustrating a method of acquiring learning dataaccording to an embodiment of the disclosure.

Referring to FIG. 7 , the first external electronic apparatus 200-1 isan electronic apparatus which received learning data for users, and wastrained for a long time, and may include an artificial intelligencemodel which includes learning data related to a plurality of users andperformed a lot of learning. The second external electronic apparatus200-2 is, for example, an electronic apparatus that a user newlypurchased, and may include an artificial intelligence model which has asmall amount of learning data for the user, and which is notpersonalized.

In this case, as illustrated in FIG. 7 , the electronic apparatus 100may input the D2 Input received from the second external electronicapparatus 200-2 into the first artificial intelligence model as an inputvalue, and output a result value. Here, when the D2 Input is input intothe first artificial intelligence model, the electronic apparatus 100may output a label value D1 Label for the D2 Input and a probabilityvalue Prob regarding whether the D1 Label is a label value for the D2Input. In case the probability value Prob is equal to or greater than apredetermined value, the selection module may train the secondartificial intelligence model based on fourth learning data includingthe D2 Input and the D1 Label.

That is, in case the second external electronic apparatus 200-2 is anelectronic apparatus newly purchased, or an electronic apparatus ofwhich internal information has been initialized, etc., the output valueand the label value for the input data are not correct. Thus, theelectronic apparatus 100 may acquire fourth learning data for the inputvalue (D2 Input) of the second learning data by using the firstartificial intelligence model wherein a lot of learning has proceeded.

FIG. 8 is a diagram illustrating a method of sharing learning dataaccording to an embodiment of the disclosure. FIG. 9 is a diagramillustrating a method for a second external electronic apparatus toconvert learning data according to an embodiment of the disclosure.

Referring to FIGS. 8 and 9 , in case the first artificial intelligencemodel and the second artificial intelligence model are artificialintelligence models for understanding of natural languages, theelectronic apparatus 100 may train the second artificial intelligencemodel by using the first learning data having similar characteristics,and train the first artificial intelligence model by using the secondlearning data.

Specifically, the electronic apparatus 100 may acquire the firstlearning data, and acquire the second learning data having an identicalor similar label to that of the first learning data from the pluralityof learning data of the second database. Alternatively, it is obviousthat the electronic apparatus 100 can acquire the second learning data,and acquire the first learning data having an identical or similar labelto that of the second learning data from the plurality of learning dataof the first database. Here, a label in an artificial intelligence modelfor understanding of a language may be a label related to the intent andentity of a user voice for a result of voice recognition.

Meanwhile, in the aforementioned various embodiments, descriptions weremade based on the embodiment wherein the electronic apparatus 100receives an artificial intelligence model and learning data of each of aplurality of external electronic apparatuses from the plurality ofexternal electronic apparatuses and acquires learning data appropriatefor another external electronic apparatus. However, the disclosure isnot limited to the aforementioned embodiment, and the aforementionedfunctions and operations of the electronic apparatus 100 can be used ineach of a plurality of external electronic apparatuses.

Specifically, as illustrated in FIG. 9 , the aforementioned functionsand operations of the electronic apparatus 100 may be performed by thefirst external electronic apparatus 200-1 or the second externalelectronic apparatus 200-2.

For example, the second external electronic apparatus 200-2 may receivea plurality of learning data stored in the first external electronicapparatus 200-1, and convert the received learning data into learningdata appropriate for the artificial intelligence model of the secondexternal electronic apparatus 200-2.

Specifically, the second external electronic apparatus 200-2 may receivea plurality of learning data stored in the first external electronicapparatus 200-1, and identify first learning data corresponding to thesecond learning data stored in the second external electronic apparatus200-2 among the plurality of learning data received from the firstexternal electronic apparatus 200-1.

Also, the second external electronic apparatus 200-2 may compare atleast one of an input value or a label value included in the secondlearning data with an input value and a label value included in each ofthe plurality of learning data received from the first externalelectronic apparatus 200-1, and identify first learning data.

Then, the second external electronic apparatus 200-2 may train thesecond artificial intelligence model used by the second externalelectronic apparatus 200-2 based on the identified first learning data.

Specifically, the second external electronic apparatus 200-2 may receivecharacteristic information of the first external electronic apparatus200-1, and convert the first learning data into third learning databased on the received characteristic information. Meanwhile, it isobvious that to the types of characteristic information and theconversion method, the same methods as the aforementioned variousembodiments can be applied. That is, characteristic information mayinclude at least one of characteristic information related to voiceinputters included in the first external electronic apparatus 200-1 andthe second external electronic apparatus 200-2, characteristicinformation related to noises input through the voice inputters of thefirst external electronic apparatus 200-1 and the second externalelectronic apparatus 200-2, or characteristic information related to thedistance between a location wherein a user voice was generated and thefirst external electronic apparatus 200-1, and the distance between alocation wherein a user voice was generated and the second externalelectronic apparatus 200-2.

FIG. 10 is a flow chart for illustrating a control method of anelectronic apparatus according to an embodiment of the disclosure.

Referring to FIG. 10 , the electronic apparatus 100 may receive anartificial intelligence model used by each of the first externalelectronic apparatus 200-1 and the second external electronic apparatus200-2 and a plurality of learning data stored in each of the firstexternal electronic apparatus 200-1 and the second external electronicapparatus 200-2 from the first external electronic apparatus and thesecond external electronic apparatus, at operation S1110. Further, theelectronic apparatus 100 can receive characteristic information of thefirst external electronic apparatus 200-1 and the second externalelectronic apparatus 200-2 and store the information.

Also, the electronic apparatus 100 may identify first learning datacorresponding to the second learning data received from the secondexternal electronic apparatus 200-2 among the plurality of learning datareceived from the first external electronic apparatus 200-1, atoperation S1120. Specifically, the electronic apparatus 100 may comparethe input value and the label value of the first learning data with theinput value and the label value of the second learning data and identifyfirst learning data.

Then, the electronic apparatus 100 may train the artificial intelligencemodel used by the second external electronic apparatus 200-2 based onthe acquired first learning data, at operation S1130. Here, theelectronic apparatus 100 can train the second artificial intelligencemodel based on third learning data which is learning data converted fromthe first learning data to a form appropriate for the second externalelectronic apparatus 200-2.

The electronic apparatus 100 may transmit the trained artificialintelligence model to the second external electronic apparatus 200-2, atoperation S1140. Through the aforementioned process, the second externalelectronic apparatus 200-2 may output a personalized result based on thesecond artificial intelligence model trained based on variouspersonalized learning data.

Meanwhile, the term “part” or “module” used in the disclosure includes aunit consisting of hardware, software, or firmware, and it may beinterchangeably used with terms such as logic, a logical block, acomponent, or a circuit. Also, “a part” or “a module” may be a componentconsisting of an integrated body or a minimum unit performing one ormore functions or a portion thereof. For example, a module may consistof an application-specific integrated circuit (ASIC).

The various embodiments of the disclosure may be implemented as softwareincluding instructions stored in machine-readable storage media, whichcan be read by machines (e.g., computers). The machines refer toapparatuses that call instructions stored in a storage medium, and canoperate according to the called instructions, and the apparatuses mayinclude an electronic apparatus according to the aforementionedembodiments (e.g., an electronic apparatus 100). In case an instructionis executed by a processor, the processor may perform a functioncorresponding to the instruction by itself, or by using other componentsunder its control. An instruction may include a code that is generatedor executed by a compiler or an interpreter. A storage medium that isreadable by machines may be provided in the form of a non-transitorystorage medium. Here, the term ‘non-transitory’ only means that astorage medium does not include signals, and is tangible, but does notindicate whether data is stored in the storage medium semi-permanentlyor temporarily.

Also, according to an embodiment of the disclosure, the methodsaccording to the various embodiments described in the disclosure may beprovided while being included in a computer program product. A computerprogram product refers to a product, and it can be traded between aseller and a buyer. A computer program product can be distributedon-line in the form of a storage medium that is readable by machines(e.g., a compact disc read only memory (CD-ROM)), or through anapplication store (e.g., Play Store™). In the case of on-linedistribution, at least a portion of a computer program product may bestored in a storage medium such as the server of the manufacturer, theserver of the application store, and the memory of the relay server atleast temporarily, or may be generated temporarily.

Further, each of the components according to the aforementioned variousembodiments (e.g., a module or a program) may consist of a singularobject or a plurality of objects. Also, among the aforementionedcorresponding sub components, some sub components may be omitted, orother sub components may be further included in the various embodiments.Generally or additionally, some components (e.g., a module or a program)may be integrated as an object, and perform the functions that wereperformed by each of the components before integration identically or ina similar manner Operations performed by a module, a program, or othercomponents according to the various embodiments may be executedsequentially, in parallel, repetitively, or heuristically. Or, at leastsome of the operations may be executed or omitted in a different order,or other operations may be added.

While the disclosure has been shown described with reference to variousembodiments thereof, it will be understood by those skilled in the artthat various changes in form and details may be made therein withoutdeparting from the spirit and scope of the disclosure as defined by theappended claims and their equivalents.

What is claimed is:
 1. A control method of an electronic apparatus, thecontrol method comprising: receiving, from a first external electronicapparatus and a second external electronic apparatus, a first artificialintelligence model and a second artificial intelligence model used bythe first and second external electronic apparatuses, respectively, anda plurality of learning data stored in the first and second externalelectronic apparatuses; identifying first learning data, whichcorresponds to second learning data received from the second externalelectronic apparatus, among learning data received from the firstexternal electronic apparatus; receiving first and second characteristicinformation of the first and second external electronic apparatuses,respectively; based on the first characteristic information of the firstexternal electronic apparatus and the second characteristic informationof the second external electronic apparatus, converting the firstlearning data into third learning data to train the second artificialintelligence model used by the second external electronic apparatus;training the second artificial intelligence model used by the secondexternal electronic apparatus based on the third learning data; andtransmitting the trained second artificial intelligence model to thesecond external electronic apparatus.
 2. The control method of claim 1,wherein the identifying of the first learning data comprises: comparingat least one of an input value or a label value included in the secondlearning data with an input value and a label value included in thelearning data received from the first external electronic apparatus; andidentifying the first learning data based on a result of the comparing.3. The control method of claim 1, wherein the second artificialintelligence model comprises an artificial intelligence model for voicerecognition, and wherein the plurality of learning data includes voicedata, a label value of the voice data, and user informationcorresponding to the voice data.
 4. The control method of claim 3,wherein the identifying of the first learning data comprises: comparingat least one of second voice data, a second label value of the secondvoice data, or second user information corresponding to the second voicedata included in the second learning data with at least one of firstvoice data, a first label value of the first voice data, and first userinformation corresponding to the first voice data included in thelearning data received from the first external electronic apparatus; andidentifying the first learning data based on a result of the comparing.5. The control method of claim 3, wherein the first and secondcharacteristic information includes at least one of characteristicinformation related to voice inputters included in the first and secondexternal electronic apparatuses, characteristic information related tonoises input through the voice inputters of the first and secondexternal electronic apparatuses, or characteristic information relatedto distances between a location where a user voice was generated andlocations of the first and second external electronic apparatuses. 6.The control method of claim 3, wherein the converting of the firstlearning data into the third learning data comprises converting voicedata included in the first learning data by using a frequency filter. 7.The control method of claim 1, wherein the receiving of the first andsecond artificial intelligence models comprises: based on apredetermined time condition, receiving the first and second artificialintelligence models used by the first and second external electronicapparatuses, respectively; receiving the plurality of learning data fromthe first and second external electronic apparatuses; and receivingfirst and second characteristic information of the first and secondexternal electronic apparatuses, respectively.
 8. An electronicapparatus comprising: a memory; a communicator; and a processorconfigured to: receive, via the communicator from a first externalelectronic apparatus and a second external electronic apparatus, a firstartificial intelligence model and a second artificial intelligence modelused by the first and second external electronic apparatuses,respectively, and a plurality of learning data stored in the first andsecond external electronic apparatuses, identify first learning data,which corresponds to second learning data received from the secondexternal electronic apparatus, among learning data received from thefirst external electronic apparatus, receive, via the communicator,first and second characteristic information of the first and secondexternal electronic apparatuses, respectively, based on the firstcharacteristic information of the first external electronic apparatusand the second characteristic information of the second externalelectronic apparatus, convert the first learning data into thirdlearning data to train the second artificial intelligence model used bythe second external electronic apparatus, train the second artificialintelligence model used by the second external electronic apparatusbased on the third learning data, and transmit, via the communicator,the trained second artificial intelligence model to the second externalelectronic apparatus.
 9. The electronic apparatus of claim 8, whereinthe processor is further configured to: compare at least one of an inputvalue or a label value included in the second learning data with aninput value and a label value included in the learning data receivedfrom the first external electronic apparatus, and identify the firstlearning data based on a result of the comparing.
 10. The electronicapparatus of claim 8, wherein the second artificial intelligence modelcomprises an artificial intelligence model for voice recognition, andwherein the plurality of learning data includes voice data, a labelvalue of the voice data, and user information corresponding to the voicedata.
 11. The electronic apparatus of claim 10, wherein the processor isfurther configured to: compare at least one of second voice data, asecond label value of the second voice data, or second user informationcorresponding to the second voice data included in the second learningdata with at least one of first voice data, a first label value of thefirst voice data, and first user information corresponding to the firstvoice data included in the learning data received from the firstexternal electronic apparatus, and identify the first learning databased on a result of the comparing.
 12. The electronic apparatus ofclaim 10, wherein the first and second characteristic informationincludes at least one of characteristic information related to voiceinputters included in the first and second external electronicapparatuses, characteristic information related to noises input throughthe voice inputters of the first and second external electronicapparatuses, or characteristic information related to distances betweena location where a user voice was generated and locations of the firstand second external electronic apparatuses.
 13. The electronic apparatusof claim 10, wherein the processor is further configured to convertvoice data included in the first learning data by using a frequencyfilter.
 14. The electronic apparatus of claim 8, wherein the processoris further configured to, based on a predetermined time condition,receive, via the communicator, the first and second artificialintelligence models used by the first and second external electronicapparatuses, respectively, the plurality of learning data from the firstand second external electronic apparatuses, and first and secondcharacteristic information of the first and second external electronicapparatuses, respectively.
 15. An electronic apparatus comprising: amemory; a communicator; and a processor configured to: receive, via thecommunicator from external electronic apparatuses, a plurality oflearning data stored in the external electronic apparatuses, identifyfirst learning data corresponding to second learning data stored in theelectronic apparatus among the plurality of learning data received fromthe external electronic apparatuses, receive, via the communicator,characteristic information of the external electronic apparatuses, basedon the characteristic information of the external electronic apparatusesand characteristic information of the electronic apparatus, convert thefirst learning data into third learning data to train an artificialintelligence model used by the electronic apparatus, and train theartificial intelligence model used by the electronic apparatus based onthe third learning data.
 16. The electronic apparatus of claim 15,wherein the processor is further configured to: compare at least one ofan input value or a label value included in the second learning datawith an input value and a label value included in the plurality oflearning data received from the external electronic apparatuses, andidentify the first learning data based on a result of the comparing. 17.The electronic apparatus of claim 15, wherein the characteristicinformation of the electronic apparatus and the external electronicapparatuses comprises at least one of characteristic information relatedto voice inputters of the electronic apparatus and the externalelectronic apparatuses, characteristic information related to noisesinput through the voice inputters of the electronic apparatus and theexternal electronic apparatuses, or characteristic information relatedto distances between a location wherein a user voice was generated andlocations of the electronic apparatus and the external electronicapparatuses.